Explainable Business Analytics for Usage-Based Auto Insurance: GAM, SHAP, and Territory Risk Segmentation in Telematics Pricing
Main article
Abstract
The shift from group-based to individual-based pricing in personal auto insurance has been accelerated by the diffusion of telematics and usage-based insurance (UBI) programmes. Although UBI portfolios are rich in behavioural signals, regulators still demand pricing models that are statistically transparent and that allow each rating relativity to be defended. This study develops a unified explainable business analytics framework that integrates Generalized Linear Models (GLM), Generalized Additive Models (GAM), gradient-boosted trees, Shapley Additive exPlanations (SHAP) and an interpretable two-dimensional K-means territory clustering procedure. The framework is applied to a synthetic UBI portfolio that replicates the distributional structure of a Canadian transactional dataset, with claim frequency and claim severity modelled separately under appropriate exponential-family distributions. Group-level RMSE shows that GAMs reduce error by roughly 33 percent relative to GLMs across the five core rating dimensions, and they better recover the curvature of risk as a function of annual mileage, credit score and years without claims. Variable-importance evidence from SHAP applied to an XGBoost model confirms credit score, years without claims and car age as the dominant frequency drivers, while annual miles driven becomes most informative once interactions with car use and region are admitted. A regularized cluster-selection criterion that augments mean absolute deviation with a complexity penalty selects between 11 and 14 territory groups, generating compact, monotonic and policy-defensible territory bands. Sensitivity analysis over the regularization parameter alpha confirms the stability of the selected K. The results provide a reproducible blueprint for regulators and insurers seeking to combine predictive performance with the interpretability required by rate-filing reviews.
